





import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


_a     :    Dict         =						logging.get_logger(__name__)
logging.set_verbosity_info()
def 							a_	(    __magic_name__		,     __magic_name__   )				->			int:




				"""simple docstring"""






				if "xprophetnet" in prophetnet_checkpoint_path:
								snake_case   :					Optional[int]    						=				XLMProphetNetForConditionalGenerationOld.from_pretrained(a_   )
								snake_case   :					List[Any]    						=				XLMProphetNetForConditionalGeneration.from_pretrained(
								    a_		,     output_loading_info=a_   )
				else:
								snake_case   :					Union[str, Any]    						=				ProphetNetForConditionalGenerationOld.from_pretrained(a_   )
								snake_case   :					str    						=				ProphetNetForConditionalGeneration.from_pretrained(
								    a_		,     output_loading_info=a_   )

				snake_case   :					Optional[int]    						=				["key_proj", "value_proj", "query_proj"]

				snake_case   :					List[str]    						=				{
				    "self_attn": "ngram_self_attn",
				    "cross_attn": "encoder_attn",
				    "cross_attn_layer_norm": "encoder_attn_layer_norm",
				    "feed_forward_layer_norm": "final_layer_norm",
				    "feed_forward": "",
				    "intermediate": "fc1",
				    "output": "fc2",
				    "key_proj": "k_proj",
				    "query_proj": "q_proj",
				    "value_proj": "v_proj",
				    "word_embeddings": "embed_tokens",
				    "embeddings_layer_norm": "emb_layer_norm",
				    "relative_pos_embeddings": "relative_linear",
				    "ngram_embeddings": "ngram_input_embed",
				    "position_embeddings": "embed_positions",
				}

				for key in loading_info["missing_keys"]:
								snake_case   :					Dict    						=				key.split('''.'''   )

								if attributes[0] == "lm_head":
												snake_case   :					Union[str, Any]    						=				prophet
												snake_case   :					Dict    						=				prophet_old
								else:
												snake_case   :					str    						=				prophet.prophetnet
												snake_case   :					Optional[Any]    						=				prophet_old.model

								snake_case   :					List[str]    						=				False
								for attribute in attributes:
												if attribute in mapping:
																snake_case   :					List[str]    						=				mapping[attribute]
																if not hasattr(a_		,     a_   ) and len(a_   ) > 0:
																				snake_case   :					Union[str, Any]    						=				attribute
												elif hasattr(a_		,     a_   ):
																snake_case   :					Union[str, Any]    						=				attribute

												if attribute == "weight":
																assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
																snake_case   :					Tuple    						=				old_model.weight
																logger.info(F"{attribute} is initialized."   )
																snake_case   :					Union[str, Any]    						=				True
																break
												elif attribute == "bias":
																assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
																snake_case   :					Tuple    						=				old_model.bias
																logger.info(F"{attribute} is initialized"   )
																snake_case   :					Tuple    						=				True
																break
												elif attribute in special_keys and hasattr(a_		,     '''in_proj_weight'''   ):
																snake_case   :					Optional[Any]    						=				old_model.in_proj_weight.shape[0] // 3
																snake_case   :					Any    						=				getattr(a_		,     a_   )
																param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
																param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
																if attribute == "query_proj":
																				snake_case   :					Dict    						=				nn.Parameter(old_model.in_proj_weight[:embed_dim, :]   )
																				snake_case   :					str    						=				nn.Parameter(old_model.in_proj_bias[:embed_dim]   )

																elif attribute == "key_proj":
																				snake_case   :					List[Any]    						=				nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]   )
																				snake_case   :					int    						=				nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]   )
																elif attribute == "value_proj":
																				snake_case   :					List[str]    						=				nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]   )
																				snake_case   :					Optional[int]    						=				nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]   )
																snake_case   :					Union[str, Any]    						=				True
																break
												elif attribute == "position_embeddings":
																assert (
																    model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
																), "Hidden size has to match"
																assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
																snake_case   :					List[Any]    						=				nn.Parameter(old_model.embed_positions.weight[:512, :]   )
																snake_case   :					int    						=				True
																break

												if attribute.isdigit():
																snake_case   :					Tuple    						=				model[int(a_   )]
																snake_case   :					int    						=				old_model[int(a_   )]
												else:
																snake_case   :					Optional[int]    						=				getattr(a_		,     a_   )

																if old_attribute == "":
																				snake_case   :					Any    						=				old_model
																else:
																				if not hasattr(a_		,     a_   ):
																								raise ValueError(F"{old_model} does not have {old_attribute}"   )
																				snake_case   :					Dict    						=				getattr(a_		,     a_   )

								if not is_key_init:
												raise ValueError(F"{key} was not correctly initialized!"   )

				print(F"Saving model to {pytorch_dump_folder_path}"   )
				prophet.save_pretrained(a_   )


if __name__ == "__main__":
			_a     :    Optional[int]         =						argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--prophetnet_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
			)
			parser.add_argument(
			    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
			)
			_a     :    Optional[Any]         =						parser.parse_args()
			convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)







'''simple docstring'''






from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


lowerCAmelCase									=   logging.get_logger(__name__)
class 		lowerCamelCase	(   _A							):



	def __init__( self		,   a_		,   a_		,   a_		,   **a_						):
			lowerCAmelCase  :     Tuple       						= feature_size
			lowerCAmelCase  :     int       						= sampling_rate
			lowerCAmelCase  :     str       						= padding_value

			lowerCAmelCase  :     int       						= kwargs.pop("padding_side"		,   "right"						)
			lowerCAmelCase  :     int       						= kwargs.pop("return_attention_mask"		,   a_						)

			super().__init__(**a_						)



	def 	_lowerCamelCase     ( self		,   a_		,   a_ = True		,   a_ = None		,   a_ = False		,   a_ = None		,   a_ = None		,   a_ = None		,   ):
			# If we have a list of dicts, let's convert it in a dict of lists
			# We do this to allow using this method as a collate_fn function in PyTorch Dataloader
			if isinstance(a_		,   (list, tuple)						) and isinstance(processed_features[0]		,   (dict, BatchFeature)						):
					lowerCAmelCase  :     str       						= {
					    key: [example[key] for example in processed_features] for key in processed_features[0].keys()
					}

			# The model's main input name, usually `input_values`, has be passed for padding
			if self.model_input_names[0] not in processed_features:
					raise ValueError(
					    "You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"
					    F''' to this method that includes {self.model_input_names[0]}, but you provided'''
					    F''' {list(processed_features.keys()						)}'''						)

			lowerCAmelCase  :     Any       						= processed_features[self.model_input_names[0]]
			lowerCAmelCase  :     Dict       						= (
			    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
			)

			if len(a_						) == 0:
					if return_attention_mask:
							lowerCAmelCase  :     List[Any]       						= []
					return processed_features

			# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
			# and rebuild them afterwards if no return_tensors is specified
			# Note that we lose the specific device the tensor may be on for PyTorch

			lowerCAmelCase  :     Optional[int]       						= required_input[0]
			if isinstance(a_		,   (list, tuple)						):
					# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
					lowerCAmelCase  :     Optional[Any]       						= 0
					while len(required_input[index]						) == 0:
							index += 1
					if index < len(a_						):
							lowerCAmelCase  :     Union[str, Any]       						= required_input[index][0]

			if return_tensors is None:
					if is_tf_tensor(a_						):
							lowerCAmelCase  :     Tuple       						= "tf"
					elif is_torch_tensor(a_						):
							lowerCAmelCase  :     str       						= "pt"
					elif isinstance(a_		,   (int, float, list, tuple, np.ndarray)						):
							lowerCAmelCase  :     Dict       						= "np"
					else:
							raise ValueError(
							    F'''type of {first_element} unknown: {type(a_						)}. '''
							    "Should be one of a python, numpy, pytorch or tensorflow object."						)

			for key, value in processed_features.items():
					if isinstance(value[0]		,   (int, float)						):
							lowerCAmelCase  :     Dict       						= to_numpy(a_						)
					else:
							lowerCAmelCase  :     Any       						= [to_numpy(a_						) for v in value]

        # Convert padding_strategy in PaddingStrategy
			lowerCAmelCase  :     Optional[int]       						= self._get_padding_strategies(padding=a_		,   max_length=a_						)

			lowerCAmelCase  :     Dict       						= processed_features[self.model_input_names[0]]

			lowerCAmelCase  :     Any       						= len(a_						)
			if not all(len(a_						) == batch_size for v in processed_features.values()						):
					raise ValueError("Some items in the output dictionary have a different batch size than others."						)

			lowerCAmelCase  :     Tuple       						= []
			for i in range(a_						):
					lowerCAmelCase  :     Any       						= {k: v[i] for k, v in processed_features.items()}
					# truncation
					lowerCAmelCase  :     Tuple       						= self._truncate(
					    a_		,   max_length=a_		,   pad_to_multiple_of=a_		,   truncation=a_		,   )
					truncated_inputs.append(a_						)

			if padding_strategy == PaddingStrategy.LONGEST:
					# make sure that `max_length` cannot be longer than the longest truncated length
					lowerCAmelCase  :     Optional[Any]       						= max(len(input_slice[self.model_input_names[0]]						) for input_slice in truncated_inputs						)
					lowerCAmelCase  :     int       						= PaddingStrategy.MAX_LENGTH

			lowerCAmelCase  :     List[Any]       						= {}
			for i in range(a_						):
					# padding
					lowerCAmelCase  :     Optional[int]       						= self._pad(
					    truncated_inputs[i]		,   max_length=a_		,   padding_strategy=a_		,   pad_to_multiple_of=a_		,   return_attention_mask=a_		,   )

					for key, value in outputs.items():
							if key not in batch_outputs:
									lowerCAmelCase  :     Dict       						= []
							if value.dtype is np.dtype(np.floataa						):
									lowerCAmelCase  :     int       						= value.astype(np.floataa						)
							batch_outputs[key].append(a_						)

			return BatchFeature(a_		,   tensor_type=a_						)



	def 	_lowerCamelCase     ( self		,   a_		,   a_ = None		,   a_ = PaddingStrategy.DO_NOT_PAD		,   a_ = None		,   a_ = None		,   ):
			lowerCAmelCase  :     Tuple       						= processed_features[self.model_input_names[0]]

			if padding_strategy == PaddingStrategy.LONGEST:
					lowerCAmelCase  :     Dict       						= len(a_						)

			if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
					lowerCAmelCase  :     int       						= ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

			lowerCAmelCase  :     List[Any]       						= padding_strategy != PaddingStrategy.DO_NOT_PAD and len(a_						) < max_length

			if return_attention_mask and "attention_mask" not in processed_features:
					lowerCAmelCase  :     Any       						= np.ones(len(a_						)		,   dtype=np.intaa						)

			if needs_to_be_padded:
					lowerCAmelCase  :     Dict       						= max_length - len(a_						)
					if self.padding_side == "right":
							if return_attention_mask:
									lowerCAmelCase  :     int       						= np.pad(
									    processed_features["attention_mask"]		,   (0, difference)						)
							lowerCAmelCase  :     Optional[int]       						= ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
							lowerCAmelCase  :     Any       						= np.pad(
							    a_		,   a_		,   "constant"		,   constant_values=self.padding_value						)
					elif self.padding_side == "left":
							if return_attention_mask:
									lowerCAmelCase  :     Dict       						= np.pad(
									    processed_features["attention_mask"]		,   (difference, 0)						)
							lowerCAmelCase  :     Any       						= ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
							lowerCAmelCase  :     Any       						= np.pad(
							    a_		,   a_		,   "constant"		,   constant_values=self.padding_value						)
					else:
							raise ValueError("Invalid padding strategy:" + str(self.padding_side						)						)

			return processed_features



	def 	_lowerCamelCase     ( self		,   a_		,   a_ = None		,   a_ = None		,   a_ = None		,   ):
			if not truncation:
					return processed_features
			elif truncation and max_length is None:
					raise ValueError("When setting ``truncation=True``, make sure that ``max_length`` is defined."						)

			lowerCAmelCase  :     int       						= processed_features[self.model_input_names[0]]

			# find `max_length` that fits `pad_to_multiple_of`
			if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
					lowerCAmelCase  :     Union[str, Any]       						= ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

			lowerCAmelCase  :     Any       						= len(a_						) > max_length

			if needs_to_be_truncated:
					lowerCAmelCase  :     Optional[int]       						= processed_features[self.model_input_names[0]][:max_length]
					if "attention_mask" in processed_features:
							lowerCAmelCase  :     Any       						= processed_features["attention_mask"][:max_length]

			return processed_features





	def 	_lowerCamelCase     ( self		,   a_=False		,   a_=None						):

			# Get padding strategy
			if padding is not False:
					if padding is True:
							lowerCAmelCase  :     str       						= PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
					elif not isinstance(a_		,   a_						):
							lowerCAmelCase  :     str       						= PaddingStrategy(a_						)
					elif isinstance(a_		,   a_						):
							lowerCAmelCase  :     Optional[int]       						= padding
			else:
					lowerCAmelCase  :     Tuple       						= PaddingStrategy.DO_NOT_PAD

			# Set max length if needed
			if max_length is None:
					if padding_strategy == PaddingStrategy.MAX_LENGTH:
							raise ValueError(
							    F'''When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined'''						)

        # Test if we have a padding value
			if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
					raise ValueError(
					    "Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"
					    " as `padding_value`. For example: `feature_extractor.padding_value = 0.0`."						)

			return padding_strategy



